{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch = [1,2,3,4,5,6,7,8]\n",
    "bidaf_dev_loss = [3.239458, 3.043728, 2.970045, 2.967297, 2.902731, 2.949542, 2.936426, 2.947496]\n",
    "bidaf_dev_f1 = [0.634481, 0.658491, 0.671131, 0.672480, 0.680892, 0.681641, 0.677987, 0.680729]\n",
    "bidaf_dev_em = [0.479596, 0.500192, 0.518768, 0.520885, 0.527045, 0.528778, 0.529259, 0.529355]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnet_dev_loss = [4.354743, 3.389990, 3.266599, 3.190507, 3.164463, 3.159744, 3.159744, 3.159744]\n",
    "rnet_dev_f1 = [0.500635, 0.623393, 0.639828, 0.648490, 0.653368, 0.656581, 0.656581, 0.656581]\n",
    "rnet_dev_em = [0.351174, 0.466596, 0.481421, 0.490855, 0.497690, 0.502118, 0.502118, 0.502118]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_dev_loss = [6.282275, 5.542525, 5.255748, 5.142156, 4.981365, 4.872141, 4.836338,4.802138]\n",
    "baseline_dev_f1 = [0.211194, 0.276784, 0.312221, 0.329886, 0.354593, 0.361624, 0.368977, 0.375016]\n",
    "baseline_dev_em = [0.136272, 0.188817,0.219036, 0.232605, 0.250986, 0.257049,0.266288, 0.270811]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure() #Creates a new figure\n",
    "ax1=fig.add_subplot(111) #Plot with: 1 row, 1 column, first subplot.\n",
    "\n",
    "line1 = ax1.plot(epoch, bidaf_dev_loss, '-r', label='BIDAF')\n",
    "line2 = ax1.plot(epoch, rnet_dev_loss, '-b', label='RNET')\n",
    "line3 = ax1.plot(epoch, baseline_dev_loss, '-g', label='Baseline')\n",
    "# plt.axis('equal')\n",
    "plt.xticks(range(1,len(epoch)+1), epoch, size='small')\n",
    "\n",
    "plt.legend()\n",
    "# plt.figsize(10,10)\n",
    "\n",
    "# plt.bar(main_ques, bidaf_list, 'red', main_ques, rnet_list, 'blue')\n",
    "plt.ylabel('Dev Loss')\n",
    "# # plt.plot(main_ques,rnet_list, color='blue')\n",
    "# # plt.axis(main_ques)\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.title('Comparison of models on Dev Loss')\n",
    "plt.savefig('plots/dev_loss_all.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure() #Creates a new figure\n",
    "ax1=fig.add_subplot(111) #Plot with: 1 row, 1 column, first subplot.\n",
    "\n",
    "line1 = ax1.plot(epoch, bidaf_dev_f1, '-r', label='BIDAF')\n",
    "line2 = ax1.plot(epoch, rnet_dev_f1, '-b', label='RNET')\n",
    "line3 = ax1.plot(epoch, baseline_dev_f1, '-g', label='Baseline')\n",
    "# plt.axis('equal')\n",
    "plt.xticks(range(1, len(epoch)+1), epoch, size='small')\n",
    "\n",
    "plt.legend()\n",
    "# plt.figsize(10,10)\n",
    "\n",
    "# plt.bar(main_ques, bidaf_list, 'red', main_ques, rnet_list, 'blue')\n",
    "plt.ylabel('Dev F1')\n",
    "# # plt.plot(main_ques,rnet_list, color='blue')\n",
    "# # plt.axis(main_ques)\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.title('Comparison of models on Dev F1')\n",
    "plt.savefig('plots/dev_f1_all.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure() #Creates a new figure\n",
    "ax1=fig.add_subplot(111) #Plot with: 1 row, 1 column, first subplot.\n",
    "\n",
    "line1 = ax1.plot(epoch, bidaf_dev_em, '-r', label='BIDAF')\n",
    "line2 = ax1.plot(epoch, rnet_dev_em, '-b', label='RNET')\n",
    "line3 = ax1.plot(epoch, baseline_dev_em, '-g', label='Baseline')\n",
    "# plt.axis('equal')\n",
    "plt.xticks(range(1, len(epoch)+1), epoch, size='small')\n",
    "\n",
    "plt.legend()\n",
    "# plt.figsize(10,10)\n",
    "\n",
    "# plt.bar(main_ques, bidaf_list, 'red', main_ques, rnet_list, 'blue')\n",
    "plt.ylabel('Dev EM')\n",
    "# # plt.plot(main_ques,rnet_list, color='blue')\n",
    "# # plt.axis(main_ques)\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.title('Comparison of models on Dev EM')\n",
    "plt.savefig('plots/dev_em_all.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#char_cnn\n",
    "\n",
    "char_cnn_dev_loss = [7.567504, 5.937887 , 5.469231, 5.324969, 5.142322, 4.988363, 4.934451, 4.873149]\n",
    "char_cnn_dev_f1 = [0.089521, 0.227565, 0.285914, 0.299790, 0.326367, 0.342589, 0.350036, 0.357305]\n",
    "char_cnn_dev_em = [0.050140, 0.159850, 0.204119, 0.214705, 0.237994, 0.250409, 0.258012, 0.261284]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1VL1D4YILXJ9ItrdgjDFAdQ+FZs3gjDPg9dchAheBGGMqh/j4eFJSUujSpQtdu3Zlzpw5Fbr+tLS0wAVuf/nLX/jhhx8qdP0VqXqHArgmpA0b4Isv/K7EGOOTmjVrkp6ezqJFi7j//vu57bbbIratF198kaOPPjpi6z9QFgqDBrlTUu2aBWMMkJ2dTcOGDQE3IM6AAQPo2rUrnTt35sMPPwRg+/btDBo0iC5dutCpUycmTJgAwIIFCzjxxBPp1q0bp512GuvXr99n/f369Qv0oFqnTh3uuOMOunTpQs+ePQPjN2RmZnLOOefQvXt3unfvzuzZs6Px0oHodYgXu5KS3LGF556DP/8E74/BGBN9Yz4dQ/qGiu06O6VFCo+dHrqjvZ07d5KSkkJubi7r16/nyy+/BCA5OZlJkyZRr149Nm3aRM+ePRk6dCiffvoprVq1CvRZlJWVRV5eHtdeey0ffvghTZs2ZcKECdxxxx289NJLpW53+/bt9OzZk/vuu4+xY8fywgsvcOedd3L99dfz97//nT59+vDbb79x2mmnsXz58op7U0KwUAA3zsITT8CECRA0yIYxpnooaj4CmDt3LpdccglLly5FVbn99tuZMWMGcXFx/PHHH2RkZNC5c2duvPFGbrnlFgYPHkzfvn1ZunQpS5cu5ZRTTgFcD6ktW7YMud2kpKTAcJ/dunXjC68Ze+rUqXsdd8jOziYnJyciw28WZ6EA0LWrG2vhlVcsFIzxUVnf6KOhV69ebNq0iczMTKZMmUJmZiYLFiwgMTGRtm3bkpubyxFHHMHChQuZMmUKd955JwMGDOCss86iY8eOzJ07N+xtJSYmIiLAnm65AQoLC/nmm29ITk6OyGsMxY4pAIi4vYV58+DHH/2uxhjjox9//JGCggIaN25MVlYWzZo1IzExkenTp7NmjRsnbN26ddSqVYuLLrqIm2++mYULF3LkkUeSmZkZCIW8vLy9Bt7ZH6eeeipPPPFE4HF6RY9GF4LtKRS58EK49VZ3zcL99/tdjTEmioqOKQCoKq+++irx8fFceOGFDBkyhM6dO5OamkqHDh0AWLJkCTfffDNxcXEkJibyzDPPkJSUxMSJE7nuuuvIysoiPz+fMWPG0LFjx/2uZ9y4cYFhOfPz8znhhBN49tlny35iBYh419kVrUK6zi7N4MFufNg1ayDEqEjGmIpjXWdXvJjtOrvSGTUK/vgDpk3zuxJjjPGFhUKwIUPcKanW7YUxppqyUAiWnAwjR8L770NWlt/VGFNtVLZm7Fh2oO+lhUJxaWmQmwvvvut3JcZUC8nJyWzevNmCoQKoKps3bz6gU1ntQHNxqnD00a7ri1mzIrcdYwzgTt1cu3Ytubm5fpdSJSQnJ9O6dWsSExP3mh7ugWY7JbU4Ebe3cOut8MsvcPjhfldkTJWWmJhIu3bt/C7DeKz5qCQXXQRxcfDaa35XYowxUWWhUJKDDoJTTnGhUFjodzXGGBM1FgqlSUuD336Dr77yuxJjjIkaC4XSnHkm1K9v4ywYY6oVC4XS1KwJI0bAe+/Btm1+V2OMMVFhoRDKqFGwYwd4Y6saY0xVZ6EQSq9e7pRU6/bCGFNNWCiEUjTOwtdfw8qVfldjjDERF7FQEJEjRSQ96JYtImOKLSMiMk5EfhWRxSLSNVL1lNvFF7twsGsWjDHVQMRCQVV/UtUUVU0BugE7gEnFFhsIHO7dRgPPRKqecjv4YDjpJLtmwRhTLUSr+WgAsEJV1xSbfibwmjrfAA1EJPRI135IS4NVq2DmTL8rMcaYiIpWKIwExpcw/SDg96DHa71pexGR0SIyX0TmZ2ZmRqjEEM46C+rWtQPOxpgqL+KhICJJwFCg3H1Rq+rzqpqqqqlNmzatuOLCVbs2DB/uutPevj362zfGmCiJxp7CQGChqmaUMO8PoE3Q49betNiTlgY5OW4AHmOMqaKiEQrnU3LTEcBk4BLvLKSeQJaqro9CTfuvTx849FDr9sIYU6VFNBREpDZwCvB+0LQrReRK7+EUYCXwK/ACcHUk6zkgRdcsTJ8Oa4ofLzfGmKohoqGgqttVtbGqZgVNe1ZVn/Xuq6peo6rtVbWzqkZwSLUKcMklbmS211/3uxJjjIkIu6J5f7RtC/36ubOQKtkwpsYYEw4Lhf01ahT8+ivMmeN3JcYYU+EsFPbXuee6U1TtgLMxpgqyUNhfdeq4YHjnHdettjHGVCEWCuUxahRkZ8MHH/hdiTHGVCgLhfI48UQ45BDr9sIYU+VYKJRHXJw7PfWLL2DtWr+rMcaYCmOhUF6jRrnTUt94w+9KjDGmwlgolFf79q7ri1desWsWjDFVhoXCgUhLg59+gnnz/K7EGGMqhIXCgRg+HGrWtAPOxpgqw0LhQNSrB2efDW+/Dbm5fldjjDEHzELhQKWlwdatMHmy35UYY8wBs1A4UP37Q+vW1u2FMaZKsFA4UPHx7pqFzz6D9bE5PpAxxoTLQqEiXHIJFBbaNQvGmErPQqEiHHkk9Opl4ywYYyo9C4WKMmoULFsGCxb4XYkxxpSbhUJFGTECatSwA87GmErNQqGiNGgAw4bB+PGwa5ff1RhjTLlYKFSktDTYsgU+/tjvSowxplwsFCrSKadAq1bW7YUxptKyUKhI8fFw0UUwZQpkZPhdjTHG7DcLhYo2ahQUFMBbb/ldiTHG7DcLhYp29NHQvbudhWSMqZQsFCIhLQ0WL4b0dL8rMcaY/RLRUBCRBiIyUUR+FJHlItKr2Px+IpIlIune7a5I1hM1I0dCUpLtLRhjKp1I7yk8Dnyqqh2ALsDyEpaZqaop3u2eCNcTHY0awdCh8OabsHu339UYY0zYIhYKIlIfOAH4L4Cq7lbVrZHaXswZNQo2bYL//c/vSowxJmyR3FNoB2QCL4vI9yLyoojULmG5XiKySET+JyIdI1hPdJ12GjRvbk1IxphKJZKhkAB0BZ5R1WOB7cCtxZZZCByiql2AJ4APSlqRiIwWkfkiMj8zMzOCJVegxER3zcLHH0NlqdkYU+2VGQoiUltE4rz7R4jIUBFJDGPda4G1qjrPezwRFxIBqpqtqjne/SlAoog0Kb4iVX1eVVNVNbVp06ZhbDpGjBoF+fmuPyRjjKkEwtlTmAEki8hBwOfAxcArZT1JVTcAv4vIkd6kAcAPwcuISAsREe9+D6+ezWFXH+s6d4auXa3bC2NMpRFOKIiq7gDOBp5W1eFAuG3/1wJvishiIAX4l4hcKSJXevPPBZaKyCJgHDBStYqNUjNqFCxcCEuW+F2JMcaUKaxQ8K4vuBD4xJsWH87KVTXda/Y5RlWHqeqfqvqsqj7rzX9SVTuqahdV7amqc8r3MmLYBRe44wu2t2CMqQTCCYUxwG3AJFVdJiKHAtMjW1YV0qQJDBrkxm/Oz/e7GmOMCanMUFDVr1V1qKo+6B1w3qSq10WhtqojLc31mvrZZ35XYowxIYVz9tFbIlLPu8ZgKfCDiNwc+dKqkIED3R6DXbNgjIlx4TQfHa2q2cAw4H+4i9IujmhVVU1SElx4IUye7EZmM8aYGBVOKCR61yUMAyarah5Qtc4Qioa0NNcP0ttv+12JMcaUKpxQeA5YDdQGZojIIUB2JIuqklJS4JhjrAnJGBPTwjnQPE5VD1LVM9RZA/SPQm1VT1oafPcd/PBDmYsaY4wfwjnQXF9E/lPU95CIPILbazD764IL3DjOds2CMSZGhdN89BKwDTjPu2UDL0eyqCqreXM44wx3zUJBgd/VGGPMPsIJhfaq+g9VXend/gkcGunCKtoPmT9w5ttnkr3L58Mho0bBunXwxRf+1mGMMSUIJxR2ikifogcicjywM3IlRUZGTgaf/PwJF75/IQWFPn5LHzzYjcxmTUjGmBgUTihcCTwlIqtFZDXwJHBFRKuKgP7t+jNu4Dg+/vlj7vzyTv8KqVEDzj8fJk2CrdVnIDpjTOUQztlHi7xBcI4BjvEGzDk84pVFwFWpV3FFtyt4YPYDvLXkLf8KSUuDXbtgwgT/ajDGmBKEPfKaNyBOUYP8oxGqJ6JEhHEDx3HiISdy+eTL+e6P7/wppFs36NjRmpCMMTGnvMNxSoVWEUVJ8UlMPG8iLeq0YNiEYazbti76RYi4A85z58JPP0V/+8YYU4ryhkKl7uaiSa0mTB45mazcLM6acBY783w4bn7RRRAXB6+9Fv1tG2NMKUoNBRFZIiKLS7gtAZpHscaI6Ny8M2+c/Qbf/vEtoz8eTdQHfGvZEk47zYWCXbNgjIkRCSHmDY5aFT4Z1mEY9/a/lzun30nnZp0Ze/zY6BaQlgYjRsD06XDyydHdtjHGlKDUUPD6OKrybu97O0s2LuHWqbfSsWlHBh0xKHobHzoUGjRwneRZKBhjYkB5jylUGSLCS2e+xLEtj+X8987nh8wodlaXnAwjR8L770O2dTxrjPFftQ8FgFqJtfhgxAfUSqzF0PFD2bIzigPhjBoFO3fCu+9Gb5vGGFOKcHpJPVtEakSjGD+1qd+GSSMm8Xv275z37nnkFeRFZ8PHHQdHHmnjLBhjYkI4ewpDgJ9F5HURGSwioQ5OV2q92vTi+cHPM23VNG78/MbobFTEHXCeNQt+/TU62zTGmFKE083FpcBhwLvA+cAKEXkx0oX5ZVTKKG7sdSNPfPsELyx4ITobvegiFw52zYIxxmdhHVPwxmX+H/A2sAA3XnOV9eDJD3L6Yadz9ZSrmbFmRuQ32Lo1nHKKC4XCwshvzxhjShHOMYWBIvIK8AtwDvAi0CLCdfkqPi6e8eeMp33D9pzzzjms3ro68hsdNQrWrIGvv478towxphTh7ClcAnwAHKmqaao6RVXzI1yX7xokN2Dy+ZPJL8znzLfPJGd3TmQ3OGwY1KtnneQZY3wVzjGF84Hvgb4AIlJTROqGs3IRaSAiE0XkRxFZLiK9is0XERknIr96XWh0Lc+LiJQjGh/BhHMnsHTjUi6ZdAmFGsGmnVq14LzzYOJEyIlwABljTCnCaT76KzAReM6b1Bq35xCOx4FPVbUD0AVYXmz+QNzYDIcDo4Fnwlxv1Jza/lQeOfURJv04iX9+9c/IbiwtDbZvh/fei+x2jDGmFOE0H10DHA9kA6jqL0Czsp4kIvWBE4D/es/brarFhxo7E3hNnW+ABiLScj/qj4rrj7uey1Iu454Z9/DusgheZNa7Nxx2mF2zYIzxTTihsEtVdxc98K5TCKdL0XZAJvCyiHwvIi+KSO1iyxwE/B70eK03bS8iMlpE5ovI/MzMzDA2XbFEhKcHPU3vNr0Z9cEovl//faQ25A44f/UVrFoVmW0YY0wI4YTC1yJyO1BTRE7BXa/wURjPSwC6As94Q3huB24tT5Gq+ryqpqpqatOmTcuzigNWI6EG75/3Pk1qNeHMt88kIycjMhu65BIXDq+/Hpn1G2NMCOGEwq24b/xLgCuAKcCdYTxvLbBWVed5jyfiQiLYH0CboMetvWkxqXmd5nw48kM279zM2e+cza78XRW/kYMPhv793VlI0R7jwRhT7YVz9lEh7sDy1ap6rqq+oGGMSKOqG4DfReRIb9IAoHgXpJOBS7yzkHoCWaq6fv9eQnQd2/JYXjnzFeb8PoerPrkqMoPzpKXBypWu6wtjjImiUCOviYjcLSKbgJ+An0QkU0Tu2o/1Xwu8KSKLgRTgXyJypYhc6c2fAqwEfgVeAK4u16uIsuEdh3PXCXfxcvrLPD7v8YrfwNlnQ506cMstkBGhZipjjCmBlPZNV0RuwJ0yOlpVV3nTDsWdNvqpqj4atSqDpKam6vz58/3Y9F4KtZDh7w7ngx8/YMoFUzjtsNMqdgNvvw2XXgqNGsGECdCnT8Wu3xhTrYjIAlVNLWu5UM1HFwPnFwUCgKquBC7CXeVcrcVJHK8Oe5VOzToxYuIIft78c8VuYORImDfPXdTWrx88+qgdYzDGRFyoUEhU1U3FJ6pqJpAYuZIqjzpJdZg8cjJJ8UkMGT+ErbnFL8M4QMccA/Pnu2E7b7jBXfFsI7QZYyIoVCjsLue8auWQBofw3nnvserPVYycOJKCwoKK3UD9+u4K54cfhkmToHt3WLq0YrdhjDGeUKHQRUSyS7htAzpHq8DKoO8hfXkLGTAlAAAZ/UlEQVR60NN8tuIzxn4xtuI3IAI33ghffun2FI47Dt54o+K3Y4yp9koNBVWNV9V6Jdzqqqo1HxXzl65/4doe1/Kfb/7DK+mvRGYjJ5wA338Pqalw8cVw1VWwKwLXShhjqq2wBtkx4fnPaf/h5ENP5oqPr2DO73Mis5EWLWDaNBg7Fp591p2VtHp1ZLZljKl2LBQqUEJcAhPOncDB9Q/m7Aln83vW72U/qVwbSoAHH4QPPoBffoGuXWHKlMhsyxhTrVgoVLBGNRsxeeRkduTtYNiEYezI2xG5jZ15JixY4LrGGDQI7roLCir4QLcxplqxUIiAo5oexdvnvs3367/n0g8vjUxXGEXat4e5c92Fbv/3fzBwIPjQk6wxpmqwUIiQMw4/gwdPfpB3lr3DfTPvi+zGataEl16CF1+EGTNcc9I330R2m8aYKslCIYJu6n0TFx9zMf9v+v9j0vJJkd/g5Ze7vYakJHem0hNP2FXQxpj9YqEQQSLC80Oep8dBPbh40sUszlgc+Y0ee6y7Cvr00+G66+D8823MZ2NM2CwUIiw5IZkPRnxA/eT6DB0/lMztUWjvb9jQnZl0//3w7rvuKugfivdabowx+7JQiIKWdVvywYgPyNiewbnvnsvugij0EhIXB7feClOnwpYt0KMHjB8f+e0aYyo1C4Uo6X5Qd/479L/MWDODa6dcG9kzkoL17++ugk5JgQsugGuvhd3WdZUxpmQWClF0QecLuK3PbTy/8Hme/u7p6G24VSuYPt31tPrkk+4g9O8RurDOGFOpWShE2b0n3cuQI4Zw/afXM23ltOhtODERHnkEJk50xxeOPRY+/zx62zfGVAoWClEWJ3G8cfYbdGjSgeHvDmfFlhXRLeCcc9zZSS1bujOU7rkHCgujW4MxJmZZKPigXo16TD5/MiLCkPFDyN4V5YFzjjjCXdx20UXwj3+4LjI2b45uDcaYmGSh4JNDGx7KxOET+Xnzz1zw3gUVPzhPWWrXhldfdT2tfvmluwr6u++iW4MxJuZYKPiof7v+jBs4jk9++YQ7vrwj+gWIwBVXwOzZ7n6fPvDMM3YVtDHVmIWCz67ufjVXdruSB2c/yJuL3/SniNRUWLgQBgyAq692A/hs3+5PLcYYX1koxIBxA8dx4iEncvnky/n2j2/9KaJRI/j4Y9fT6ltvuSE/f/rJn1qMMb6xUIgBifGJTDxvIi3rtmTY28NYt22dP4XExcGdd8Jnn0FGhtuDmDjRn1qMMb6wUIgRTWo1YfLIyWzbvY1hbw9jZ95O/4o55RTXnNSpEwwfDn//O+Tl+VePMSZqLBRiSOfmnXnjrDf4bt13/PWjv0avK4yStGkDX3/telp97DHo1w/++MO/eowxUWGhEGPO7HAm9/a/lzeXvMnoj0azNnutf8UkJcHjj8Pbb8OiRe4q6GlRvArbGBN1EQ0FEVktIktEJF1E5pcwv5+IZHnz00XkrkjWU1nc3vd2rj/uel5Of5lDHz+Uyz+8nJ82+XjQd8QIdw1DkyZw6qnwr3/ZVdDGVFHR2FPor6opqppayvyZ3vwUVb0nCvXEPBHhsdMf49frfmV0t9G8tfQtjnrqKM5951wWrFvgT1FHHQXffusC4o47YOhQ+PNPf2oxxkSMNR/FsLYN2vLkGU+y+vrV3NbnNqaunErqC6mc+vqpTF81PfrHHOrUgTffdD2tfv65uwp6gU8hZYyJiEiHggKfi8gCERldyjK9RGSRiPxPRDqWtICIjBaR+SIyPzMzCiOXxZjmdZpz34D7WDNmDQ8MeIDFGYs56bWT6Pnfnnzw4wcUahSbckTgmmtg5kwoKIDjj4cXXrCroI2pIiSS3zZF5CBV/UNEmgFfANeq6oyg+fWAQlXNEZEzgMdV9fBQ60xNTdX58/c5PFGt5Obn8kr6K/x79r9ZtXUVRzU5iluOv4ULOl9AYnxi9ArZtAkuvNDtNZx/PowaBb16Qb160avBGBMWEVkQohl/z3LRaoIQkbuBHFV9OMQyq4FUVd1U2jIWCnvkF+bz7rJ3eWC223s4uP7B3NTrJi7vejm1EmtFp4iCAncV9H33QX6+uwAuJcX1o9S3r7s1bx6dWowxpfI9FESkNhCnqtu8+18A96jqp0HLtAAyVFVFpAcwEThEQxRlobAvVWXKL1O4f9b9zP59Nk1qNeH6467nmu7X0LBmw+gUkZPjuuOeOdPdvvkGdnoX4B1++N4h0b69a4YyxkRNLITCocAk72EC8Jaq3iciVwKo6rMi8jfgKiAf2AncoKpzQq3XQiG0Wb/N4v5Z9zPllynUSarDld2u5IZeN9CybsvoFrJ7txsbuigkZs2CLVvcvBYt9g6JY46B+Pjo1mdMNeN7KESKhUJ4Fm1YxAOzH+CdZe+QEJdAWpc0bj7+Zg5rdJg/BRUWwo8/7gmJmTPht9/cvLp1oXdvFxB9+kCPHlCzpj91GlNFWSgYAFZsWcFDcx7i5fSXyS/MZ/jRw7m1z62ktEjxuzQXCrNm7QmJZcvc9KQk1xlfUUgcfzw0jFIzmDFVlIWC2cv6bet59JtHeWb+M+TszmHgYQO5tc+t9D24LxIr7ftbtrgBf4qam+bPdx3xibjO+YpCom9faN3a72qNqVQsFEyJ/tz5J09/9zSPzXuMTTs20btNb27rcxuDDh8UO+FQZMcOdxV10d7EnDnugDZA27Z7h0SHDnbw2pgQLBRMSDvydvDS9y/x0JyH+C3rNzo168Stx9/KiE4jSIhL8Lu8kuXnu475gpucNm508xo33vvg9bHHQmIUr9kwJsZZKJiw5BXkMX7peB6c/SA/ZP5A2wZtubn3zVyacik1E2P8YK8q/PLL3iGxYoWbV6sW9Oy5JyR69oTatf2t1xgfWSiY/VKohXz000fcP+t+5v0xj+a1mzOm5xiuSr2K+sn1/S4vfOvX7x0Sixa58IiPd301FYXEiSfawWtTrVgomHJRVb5e8zX3z7qfz1d8Tr0a9bg69WrG9BxD8zqV8MrkrCyYO3dPSHz7Leza5UKiTx8YMsTdjjjC70qNiSgLBXPAFqxbwAOzH+C9H96jRkINLku5jJt630S7hu38Lq38du1yY0P873/w0UewZImbfsQRewLi+OMhIUaPqxhTThYKpsL8vPln/j3737y26DUKtZCRnUZya59b6dSsk9+lHbjVq+Hjj11AfPWVuxK7QQMYONAFxOmnWzOTqRIsFEyFW5u9lkfnPspzC55je952Bh8xmNv63EbvNr39Lq1ibNvmenz96CP45BPXC2x8vDsGMWQIDB5szUym0rJQMBGzecdmnvz2ScZ9O44tO7dwwiEncGOvG+l7cN/odcAXaQUF7vjDRx+529Klbro1M5lKykLBRNz23dt5YeELPDL3EdZmrwXgkPqHkNIihS7Nu5DSIoWUFim0bdA29i6M21+rV7tw+PhjmD7dXWndsOHezUwNGvhdpTGlslAwUbO7YDfTV00nfUM66RnpLNqwiJ82/xQYEa5ejXr7BEXHph2pkVDD58rLqaxmpiFDXHfhxsQQCwXjqx15O1i6cakLig3pLMpYxKINi9ietx2AhLgEjmpyFF1adCGluQuKLi260KRWE58r308FBTBv3p69iKJmpiOP3BMQvXtbM5PxnYWCiTmFWsiKLSv2Cor0Den8se2PwDKt67Xea48ipUUKhzY8lDiJ9HDiFWTVqr3PZipqZjrjDHeg2pqZjE8sFEylkbk9MxAQRT+XZy6nQAsAqJNUh2OaHxPYo0hpkUKnZp1ivxuO7Ow9zUxTprhmpoSEvZuZDvNpfAtT7VgomEotNz+XZRuX7RUU6RvS2bZ7GwBxEseRjY/ca4+iS/MusXvVdUGBG6K0aC+iaOyIDh3cHoQ1M5kIs1AwVU6hFrJ662oXFBsWkZ7hguK3rN8Cy7So08KFRNBexWGNDiM+LsaG+1y5ck9AfP21a2Zq1Gjvs5nqV6I+p0zMs1Aw1caWnVtYtGHRXnsUyzKXkV+YD0CtxFp0btZ5rz2KY1seS3JCss+Ve7Kz4bPPXEh88gls3uz2GE44we1FDB7smpkq+2m9xlcWCqZa25W/i+Wblrs9Cu9U2fQN6WzN3QpAckIyx7c5npMPPZmTDz2ZY1scGxt7E0XNTEUXzf3wg5vepg306wf9+7tb27Z+VmkqIQsFY4pRVX7L+o3vN3zPjDUzmLpyKks2ug7xGiY3pH+7/pzc7mQGHDqAwxsdHhsX3K1cCZ9+6i6Y++ord7AaXCj0778nKNq08bFIUxlYKBgThoycDL5c9SVTV05l6qqpgeMTbeq14eRDT2ZAuwEMOHQALeq08LlSoLDQHaD+6isXEl9/7ca1Bmjffs9eRL9+0KqVn5WaGGShYMx+UlVW/LnCBcTKqXy56kv+zP0TgE7NOjGg3QBOPvRkTjzkROrWqOtztbiQWLx4z17E11+78SPA9dEUHBLNY/SsLBM1FgrGHKCCwgLSN6QzdeVUpq2axszfZpKbn0tCXAI9DurBye3c8YjjWh9HUnyS3+W64xHp6XtCYsYM1yUHwFFH7R0STSrZlePmgFkoGFPBcvNzmfv73EBT0/x18ynUQmon1uaEQ04INDd1bt45Nq7Azs+HhQtdSEyf7oYp3e66GaFz5z0BceKJ7nRYU6VZKBgTYVtzt/LV6q8CexI/bvoRgKa1mjLg0AGB5qa2Ddr6W2iRvDyYP39PSMyeDTt3ulNdu3TZExInnGBdcVRBFgrGRNna7LVMWzmNaaumMXXlVNbnrAegfcP2gYDo365/7HT6VzQ0aVFIzJnjpsXFwbHH7mlu6tMH6tXzu1pzgGIiFERkNbANKADyixck7py/x4EzgB1AmqouDLVOCwVTGagqyzctZ9rKaUxdNZXpq6azbfc2BCGlRUrg+og+B/ehVmItv8t1cnPdNRJFZzd9840bnjQ+Hrp12xMSxx8Pder4Xa3ZT7EUCqmquqmU+WcA1+JC4TjgcVU9LtQ6LRRMZZRfmM/8dfMDZzbN+X0OeYV5JMUn0btN78D1EamtUkmIi5H+j3bsgLlz94TEvHnuOEVCAvTosecaid69oVaMBJspVWUJheeAr1R1vPf4J6Cfqq4vbZ0WCqYq2L57O7N+mxU4HvH9hu8BNyBR/7b9A81NHZp0iI2L6MAdpJ49e8/ZTd995854SkqC447bc0zi6KPd2U3xMXCFuAmIlVBYBfwJKPCcqj5fbP7HwAOqOst7PA24RVXnF1tuNDAa4OCDD+62Zs2aiNVsjB8yt2cyffX0QHPTyj9XAtCqbiv6te3HsS2O5Zjmx3BM82NoXrt5bATFtm3ujKaiYxILF7prJ8AdvG7SxF0f0ayZ+1l0C35cdL9GJR2FrxKJlVA4SFX/EJFmwBfAtao6I2h+WKEQzPYUTHWw6s9VgQPWM3+bybpt6wLzmtZqGgiIotvRTY/2v4O/rCy3J7FqFWRkwMaN7mfw/Zyckp9bv37JYVHS/bp1rXPAcoiJUNhrQyJ3Azmq+nDQNGs+MiYMm3dsZsnGJSzOWBy4Ld24lJ35OwGIl3iOaHzEPmHRpl6b2NirKLJjx95hUVp4ZGTs6cKjuOTk8MKjeXN3/UVcDFwzEgN8DwURqQ3Eqeo27/4XwD2q+mnQMoOAv7HnQPM4Ve0Rar0WCsY4BYUFrPhzxV5BsThjMau2rgosU79G/X2ColOzTtRJqgRnD+XlQWZm2eGxcaO7FRTsu474eGjatPTgaNHC9RPVsiU0blyl90BiIRQOBSZ5DxOAt1T1PhG5EkBVn/VOSX0SOB13SuqloZqOwELBmLJk78pm6cal+4RF0ah14K6dKB4WlWos7OIKC92eRVnhUXQ/N3ffdSQluXBo1WpPUBTdD57WsGGlDA/fQyFSLBSM2X+qypqsNfsExS9bfqFQ3cHhosGIgoOic7PONKzZ0OfqK5iqO0i+cSNs2ADr18O6dXvfiqYVdTAYrEaNfYOipMf168dUeFgoGGPKtCNvBz9k/rBXUCzKWMSWnXva89vUa7PPXsURjY+InespImn7dhcQoYJj3bo9HQ8Gq1mz7L2OVq2iduDcQsEYUy6qyvqc9fvsVSzftDwwxGmN+Boc3fTofcKiWe1mPlfvk5ycsoNj3bo9HRIGq107vPA4wKvILRSMMRVqd8Fuftz04z5hUdTHE0Cz2s1oXa819WvUp35yffcz+H7QzwbJDfaalpyQHFtnSkXCtm1lB8e6da6jwuLq1oWxY+HOO8u16XBDoRrs/xljKkJSfFJgjyBY5vbMwOmySzKWkLE9g6xdWazYsoKsXVlk5WaRvSsbJfQX0MS4xBLDo0oFS926cOSR7lYaVcjOLjk4OnWKeIm2p2CMibhCLSRndw5ZuVmBoNiauzVwf6+fJU2LYLDUr1GfejXqUT/Z/aydWDu2g6WcbE/BGBMz4iSOejXqUa9GPdrQplzrCA6WUgOlhGDZ3z2W4FqLB0a9pD33i+YFzw+eFvN7LaWwUDDGVAp7BUv9AwuWrblbA6GRvSs7cCsKjuDpWbuy2Lh9I79s/iUwrehK8lAS4xL3DpTgkCkjUIKnJcYnluu1lpeFgjGm2ggOFuqXfz27C3azbde2vcOjpEDJzSJ79555a7PXBuZn5WaRV5hX5raSE5IDAXFl6pXc0OuG8hceBgsFY4zZT0nxSTSu1ZjGtRqXex2qyq6CXaEDpdi0FnVaVOCrKJmFgjHG+EBESE5IJjkhOaau76ikHZ0YY4yJBAsFY4wxARYKxhhjAiwUjDHGBFgoGGOMCbBQMMYYE2ChYIwxJsBCwRhjTECl6yVVRDKBNeV8ehNgUwWWE2mVqd7KVCtUrnorU61QueqtTLXCgdV7iKo2LWuhShcKB0JE5ofTdWysqEz1VqZaoXLVW5lqhcpVb2WqFaJTrzUfGWOMCbBQMMYYE1DdQuF5vwvYT5Wp3spUK1SueitTrVC56q1MtUIU6q1WxxSMMcaEVt32FIwxxoRgoWCMMSag2oSCiNQXkW9FJEdEOvldTygi0kNE5orIDBEZLyLRHaR1P4hIcxGZIyJfi8iXItLS75rCISLne9e8xCwRaSsimSLylXcr8xxzv4lIPxGZJiLTReQsv+spjYj0CnpffxaRR/2uKRQRiRORV0RkpojMEpEOkdpWtQkFYAcwCJjodyFh+B04SVVPAFYDZ/pbTkibgD6qeiLwGnC5z/WUSUTigeG49znWfa2q/bxbrIdYTeBGYKCq9lfVSX7XVBpVnVv0vgJzgA98LqksKUANVe0L3AZEbKDmahMKqpoX6/9URVR1varu9B7uBgr9rCcUVS1Q1aL66gLL/KwnTOcD7xLD72uQ471vh/8SEfG7mDL0AnYCH4nIJBGJ/IDCB0hEkoAewEy/aynDWkC8v4GGRPAq7GoTCpWRiBwCnAp85HctoYhIiojMA/4GLPS7nlC8vYTzgAl+1xKG9cBhwAlAM+Bsf8spU3NcvUOAF4C7fa0mPCcD04K+2MSqTUAe8CPwBPB0pDZkoRCjRKQe8DqQpqp5ftcTiqqmq+pxwP/D7drGsouAdyrBhwCquktVt6s7b/x9oIvfNZVhKzBbVXcD04COPtcTjuG4vcZYdyqQr6pHAucAj0RqQxYKMUhEEoC3gX+q6k9+1xOKt/tdJAt37CaWHQ1cIiKfAoeLyDi/CyqNiNQNetgX+NWvWsL0HXCU18SRAqz0uZ6QvBM4ugOz/K4lDAJs9u5vAupHbEPV6eI1EZmC+2NdAzynqq/4W1HJRORi4DFgiTfpGVWNyeYOEekBPAwUALnAZaq63t+qwhPrnaGJyEDgXlzQrsK9t/n+VhWaiFwDjAAUV+8Kn0sqlff+DlTV6/yupSzeF8U3gRZADeAGVZ0TkW1Vp1AwxhgTmjUfGWOMCbBQMMYYE2ChYIwxJsBCwRhjTICFgjHGmAALBVMliIiKyCNBj28SkbsjsJ2HRGSZiDxUbHqa13ldetDt6Arc7t0iclNFrc+Y0iT4XYAxFWQXcLaI3K+qEesXBhgNNFLVghLmTVDVv0Vw28ZEnO0pmKoiHzdU4d+Lz/C6oP5SRBZ73TofHGpF4jwkIktFZImIjPCmTwbqAAuKppXF60p6hoh8IiI/icizIhLnzTvfW/9SEXkw6Dmni8hCEVkkItOCVne019XzShG5zlu2trfuRd56wqrLmNLYnoKpSp4CFovIv4tNfwJ4VVVfFZHLgHHAsBDrORt35XsXoAnwnYjMUNWhIpKjqimlPG+EiPQJetzL+9kD173GGuBT3B7NHOBBoBvwJ/C5iAwDZuM6kztBVVeJSKOg9XUA+uN6o/1JRJ4BTgfWqeogcOOGhHhdxpTJ9hRMlaGq2bgxHYp3W9ALeMu7/zrQh9D6AOO9bsEzgK9xfeSUZYKqpgTdiro//1ZVV3pNTuO99XcHvlLVTK/rijdxvaH2BGao6irvNW0JWv8nXid5m4CNuF5JlwCniMiDItJXVbPCqNOYUlkomKrmMdxAP7X9LiRI8b5kytu3zK6g+wVAgqr+DHTFhcO9InJXOddtDGChYKoY75v1O+w9AtwcYKR3/0LKHlBlJq4pKN4bAvME4NsDKKuHiLTzjiWMwPXK+S1woog08cZ4OB+3R/INcIKItAMo1ny0DxFpBexQ1TeAh3ABYUy52TEFUxU9ghvwp8i1wMsicjOQCVwKICJDgVRVLf7tehKuyWkR7lv9WFXdEMZ2ix9TuNr7+R3wJG4AmunAJFUtFJFbvceCaxr60KtrNPC+FyIbgVNCbLMz8JCIFOIGYbkqjDqNKZX1kmpMBIlIP+AmVR3sdy3GhMOaj4wxxgTYnoIxxpgA21MwxhgTYKFgjDEmwELBGGNMgIWCMcaYAAsFY4wxAf8f+P/O/Xs+ocYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure() #Creates a new figure\n",
    "ax1=fig.add_subplot(111) #Plot with: 1 row, 1 column, first subplot.\n",
    "\n",
    "line1 = ax1.plot(epoch, char_cnn_dev_loss, '-r', label='Char-CNN')\n",
    "line2 = ax1.plot(epoch, baseline_dev_loss, '-g', label='Baseline')\n",
    "# plt.axis('equal')\n",
    "plt.xticks(range(1,len(epoch)+1), epoch, size='small')\n",
    "\n",
    "plt.legend()\n",
    "# plt.figsize(10,10)\n",
    "\n",
    "# plt.bar(main_ques, bidaf_list, 'red', main_ques, rnet_list, 'blue')\n",
    "plt.ylabel('Dev Loss')\n",
    "# # plt.plot(main_ques,rnet_list, color='blue')\n",
    "# # plt.axis(main_ques)\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.title('Comparison of Char-CNN on Dev Loss')\n",
    "plt.savefig('plots/dev_loss_charcnn.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure() #Creates a new figure\n",
    "ax1=fig.add_subplot(111) #Plot with: 1 row, 1 column, first subplot.\n",
    "\n",
    "line1 = ax1.plot(epoch, char_cnn_dev_f1, '-r', label='Char-CNN')\n",
    "line2 = ax1.plot(epoch, baseline_dev_f1, '-g', label='Baseline')\n",
    "# plt.axis('equal')\n",
    "plt.xticks(range(1,len(epoch)+1), epoch, size='small')\n",
    "\n",
    "plt.legend()\n",
    "# plt.figsize(10,10)\n",
    "\n",
    "# plt.bar(main_ques, bidaf_list, 'red', main_ques, rnet_list, 'blue')\n",
    "plt.ylabel('Dev F1')\n",
    "# # plt.plot(main_ques,rnet_list, color='blue')\n",
    "# # plt.axis(main_ques)\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.title('Comparison of Char-CNN on Dev F1')\n",
    "plt.savefig('plots/dev_f1_charcnn.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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